A bibliometric analysis of the Journal of Molecular Graphics and Modelling: An update.

J Mol Graph Model

Information School, University of Sheffield, 211 Portobello, Sheffield, S1 4DP, UK. Electronic address:

Published: December 2022

This paper provides a bibliometric review of the articles published in the Journal of Molecular Graphics and Modelling (formerly the Journal of Molecular Graphics). The journal has grown rapidly since its establishment in 1983, with articles coming from countries throughout the world. It now primarily contains articles describing applications of molecular graphics and modelling in chemical and biological systems, rather than the underlying technology that was the focus of many of the early papers in the journal; however, it is these early, system-based papers that continue to attract by far the largest numbers of citations.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jmgm.2022.108313DOI Listing

Publication Analysis

Top Keywords

molecular graphics
16
journal molecular
12
graphics modelling
12
journal
5
bibliometric analysis
4
analysis journal
4
molecular
4
graphics
4
modelling update
4
update paper
4

Similar Publications

Background: Haplotype networks are useful for investigating the genealogical relationships among haplotypes and have been extensively used in molecular evolution and population genetic studies. However, existing tools for visualizing haplotype networks lack comprehensive functionalities for data exploration and layout adjustments. Therefore, there is a need for a useful tool capable of constructing and visualizing haplotype networks, facilitating the exploration of data, particularly with datasets with large sample sizes.

View Article and Find Full Text PDF

Phase change materials such as Ge2Sb2Te5 (GST) are ideal candidates for next-generation, non-volatile, solid-state memory due to the ability to retain binary data in the amorphous and crystal phases and rapidly transition between these phases to write/erase information. Thus, there is wide interest in using molecular modeling to study GST. Recently, a Gaussian Approximation Potential (GAP) was trained for GST to reproduce Density Functional Theory (DFT) energies and forces at a fraction of the computational cost [Zhou et al.

View Article and Find Full Text PDF

Biological data visualization is challenged by the growing complexity of datasets. Traditional single-data plots or simple juxtapositions often fail to fully capture dataset intricacies and interrelations. To address this, we introduce "cross-layout," a novel visualization paradigm that integrates multiple plot types in a cross-like structure, with a central main plot surrounded by secondary plots for enhanced contextualization and interrelation insights.

View Article and Find Full Text PDF

Solvatochromic charge model of isonitrile probes for investigating hydrogen-bond dynamics with 2DIR spectroscopy.

J Chem Phys

January 2025

Department of Chemistry - Ångström Laboratory, Uppsala University, SE-75120 Uppsala, Sweden.

Isonitrile-derivatized amino acids are emerging as highly effective infrared (IR) probes for investigating the structures and dynamics of hydrogen (H)-bonds. These probes enable the quantification of chemical exchange processes in solute-solvent complexes via two-dimensional IR spectroscopy and hold significant promise for site-specific dynamic studies within proteins. Despite their potential, theoretical models that elucidate the solvatochromism of isonitriles remain underdeveloped.

View Article and Find Full Text PDF

The expanding scale and complexity of microscopy image datasets require accelerated analytical workflows. NanoPyx meets this need through an adaptive framework enhanced for high-speed analysis. At the core of NanoPyx, the Liquid Engine dynamically generates optimized central processing unit and graphics processing unit code variations, learning and predicting the fastest based on input data and hardware.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!